regional dynamics in an industrial region of south gujarat ... · regional dynamics in an...
TRANSCRIPT
INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES
Volume 5, No 4, 2015
© Copyright by the authors - Licensee IPA- Under Creative Commons license 3.0
Research article ISSN 0976 – 4380
Submitted on January 2015 published on May 2015 592
Regional dynamics in an industrial region of south Gujarat, India
-A geospatial perspective Bikramjit Barooah1, Tathagata Ghosh2, Rolee Kanchan3
1- Research Student, Department of Geography, Faculty of Science, The Maharaja Sayajirao
University of Baroda, Vaodara-390002, Gujarat
2- Temporary Assistant Professor, Department of Geography, Faculty of Science, The
Maharaja Sayajirao University of Baroda, Vaodara-390002, Gujarat
3- Professor, Department of Geography, Faculty of Science,The Maharaja Sayajirao
University of Baroda, Vaodara-390002, Gujarat
ABSTRACT
The process of industrialization and urbanization puts its effect on its immediate surroundings
leading to transformation in Landuse and Landcover. In the present study, an attempt has
been made to analyze the Landuse and Landcover dynamics of south Gujarat in the last two
decades (1990 to 2011). Traditional supervised and unsupervised classification along with the
band rationing technique has been employed to understand the spatial pattern of different
Landuse and Landcover categories. Broad categories like built-up land, agricultural land,
vegetation, water body, fallow land and riverine sediments were extracted from the analysis.
The built-up land and agricultural land showed significant changes where, former was related
to positive change while the later detected negative changes. The band rationing techniques
assisted in determining the changes that has taken place in each of the category and
correlation among these categories showed important inter-relationship.
Keyword: Regional dynamics, land use and land cover, band rationing.
1. Introduction
Regional dynamics in response to the fast economic and social transformation has its impact
on the immediate surroundings. In the last few decades, the accelerated process of
industrialization and higher rate of population growth resulted into large scale impact on land
use and land cover over the earth surface. Urban centers are the major hubs of the population
concentration where economic and industrial activities are coagulated. These growth centers
and growth poles play key role in initiating the process of industrialization and urbanization.
The spread effect from the centers to the outer peripheral region gradually transforms the
scenario. This transformation has an influence on the existing vegetation cover, transport
system, settlements etc. This type of transformation generates employment, increase the
density, intensity and quality infrastructure and better livelihood. One of the major focus of
the Country after the Independence was to attain self sufficiency in the sector of agriculture
and accelerate the process of industrialization to attain balanced regional development which
brings changes in the agricultural policies and industrial settings. Hence, it is essential to
analyze the changing scenario of land use and land cover as they are direct representative of
the socio-economic transformation of the region. Li and Yeh (2003) analysed the land use
and land cover changes in the Pearl river delta of South China. The result illustrated that the
market economy has played an important role in reconstructing the land use pattern. To
depict the spatial pattern indicators like compactness index and entropy were used in the
Regional dynamics in an industrial region of south Gujarat, india –A geospatial perspective
Bikramjit Barooah et al.,
International Journal of Geomatics and Geosciences
Volume 5 Issue 4, 2015 593
study. Similar type of work has been done by Deng et al. (2009) in the Zeijhihan province of
China. In this study, other than using traditional remote sensing techniques, different matrixes
like Shannon’s diversity index, Mean Patch Size etc. were taken into consideration. The
results revealed the applicability of both the methods and also identified the landscape pattern
dynamics in different time periods. Besides using conventional land classification methods by
supervised and unsupervised classification, Porter-Bolland et al. (2007) adopted statistical
techniques like principal component analysis and image differentiations for landuse and
landconver pattern in the La-montana region. Prakash and Gupta (1998) analyzed three
methods viz. image differentiation, image rationing and NDVI and no significant variations
in results were observed among these methods. Indices like NDVI, SAVI, PCA and KT was
analyzed by Rogan and Yool (2001) in Pelonciollo mountains of Arizona and New Mexico. It
was found that KT provided the best result among all of the indexes. The combinations of
traditional unsupervised classification with GIS based spatial reclassification and post
classification provided better results in the study of Yang and Lo (2002) in the metropolitan
area of Atlanta, Georgia.
Visual image interpretation is one of the method that was used from 1970’s when the satellite
images were not available. Loveland et al. (2002) applied the visual interpretation technique
on fine scale MSS, TM and ETM+ to identify the land use and land cover change rate. Zhu
and Woodcock (2014) implemented Continuous Change Detection and Classification
(CCDC) technique in the parts of New England. In this technique, observed and predicted
images were taken into considerations using Landsat satellite images. As the pixel value
exceeds the threshold value a pixel was generated and the algorithm exhibited 90% accuracy.
For the identification of detection of land cover disturbances in the Lappi reindeer herding
district, Kivinen and Kumpula (2014) adopted traditional method of land use and land cover
using multiple sources like MSS, TM, aerial photographs. The analysis suggested that since
late 1960’s the area under study experienced significant disturbances which are about 27% of
the total area. Rawat et al. (2013) attempted land use and land cover change detection in the
parts of Uttarakhand state of India using geospatial technique. According to the study, built-
up land and agricultural area showed significant change particularly in the south of the
National Highway no. 121.
In the present study, an attempt has been made to incorporate different types of techniques to
observe the changes that have taken place in the last two decades (1990-2011).
2. Study area
The study area is located in the southern portion of the state of Gujarat. Geographically, the
region extends between 72o56’38” E to 73o5’22” E longitude and 21o39’25” N to 21o46’14”
N latitude covering an area of 192.33 km2 (Figure.1) (DCHB 2001). The west of the study
area is bounded by Gulf of Khambat. In the north and south, river Mahi and Narmada flows
in the east to west direction respectively. Physiographically, the entire region is considered as
flat terrain with an elevation ranging from m 17 to 24 m (SOI toposheet No. 46C/14 and
46G/2). The National Highway No. 08 traverses through the study area and is a part of
Golden Corridor. This is one of the major reasons behind the faster rate of industrialization in
and around the city of Bharuch. The major industries of this region are chemical,
petrochemical, pharmaceuticals, engineering and textile.
Regional dynamics in an industrial region of south Gujarat, india –A geospatial perspective
Bikramjit Barooah et al.,
International Journal of Geomatics and Geosciences
Volume 5 Issue 4, 2015 594
Figure 1: Study area
3. Methodology
Present study mainly depends upon Landsat satellite image of 1990 and 2011. Both the
images of 14th November (Row/Path 148/45) were downloaded from the USGS website
(http://glovis.usgs.gov/). First three visible bands (Band-1=Blue, Band-2=Green and Band-
3=Red) were stacked. From the stacked images, area of interest was subset (72o56’38” E to
73o5’22” E longitude and 21o39’25” N to 21o46’14” N latitude). Survey of India (SOI)
toposheet No 46C/14 and 46G/2 were used for image registration. Initially, unsupervised
classification was done on both the images with higher number of classes, later on the basis
of visual interpretation similar classes were merged with each other. After the completion of
the unsupervised classification, supervised classification was done. Finally, seven classes
were identified on both the images (vegetation cover, agricultural land, water body, fallow
land, reverie sediments and built up land) and their area in km2 was calculated. Band
rationing techniques like Normalized Difference Vegetation Index (NDVI), Normalized
Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI) and
Normalized Difference Bareness Index (NDBaI) were adopted. 1500 pixels were randomly
generated on each of the indices and its corresponding pixel values were extracted (Figure.2).
The extracted pixel values were used to map in GIS platform to depict the spatial distribution
of the indices using kriging interpolation method. Multiple correlation among the indices also
been done for both the images. ERDAS Imagine 9.1 and ArcGIS 10 was used for the image
processing and mapping respectively.
3.1 Band Rationing Indices
Band rationing is the method where a particular portion of the bandwidth is taken into
consideration. Every entity on the surface of the earth reflects the incoming solar radiation at
Regional dynamics in an industrial region of south Gujarat, india –A geospatial perspective
Bikramjit Barooah et al.,
International Journal of Geomatics and Geosciences
Volume 5 Issue 4, 2015 595
a particular wavelength. Thus, that particular wavelength will depict much better information
than the entire wavelength.
Figure 2: Workflow of the study
On the basis of this concept, number of band ratios have been evolved to depict the
corresponding entity of the earth surface. Some of the common band ratio indices are
Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index
(NDBI), Normalized Difference Bareness Index (NDBaI) and Normalized Difference Water
Index (NDWI). The calculation of the indexes are as follows
Li and Fox (2012)
Regional dynamics in an industrial region of south Gujarat, india –A geospatial perspective
Bikramjit Barooah et al.,
International Journal of Geomatics and Geosciences
Volume 5 Issue 4, 2015 596
Zhang et al. (2009)
Maki et al. (2004)
Chen et al. (2006)
Where –
NDVI= Normalized Difference Vegetation Index,
NDBI= Normalized Difference Built-up Index,
NDWI= Normalized Difference Water Index,
NDBaI= Normalized Difference Bareness Index,
NIR=Near Infrared, SIR=Shortwave Infrared.
4. Results and discussion
4.1 Spatial Pattern of land use and land cover
The spatial pattern of the land use and land cover showed significant variations during the
study period. A large patch of build up land was observed in the central portion of the study
area mostly confined in the northern bank of the river Narmada. In recent years, the patch
was sprawled from the central to the outward periphery in all the directions except in the
south. Some smaller patches can also be observed in the north central portion which had
grown as an outgrowth in the recent years. In the south eastern segment of the study area
some smaller patches of settlements have recently emerged. Conspicuous changes can be
observed in the agricultural land. In the former year (1990) the agricultural land was mostly
observed in the northern part of the river. In a span of two decades new settlements have
emerged and they occupied a significant portion resulting into a decline in the proportion of
agricultural land. On the other hand, in recent years the agricultural land was largely confined
to the south of river Narmada. Some small patches of agricultural land had also emerged on
the small deposition on bed of river Narmada located in the eastern part. The major segment
of vegetated area had shifted from the northern portion of the study area to the south of the
river in continuous patch. Similarly, the fallow land had dispersed and confined to the south
eastern part of the study area.
4.2 Land use / land cover and band rationing
In 1990, 15.62 km2 area was covered by built-up land which was about 8.12% to the total
area. In 2011, this area increased to 30.49 km2 (15.85%) (Figure.3). Thus, in the time span of
21 years an increase of 7.73% was observed where as a decline was observed in agricultural
land. In 1990, 79.44 km2 (41.30% of the total area) was under agricultural land while in 2011
the area declined to 72.39 km2 (37.64% of the total area) (Table.1). Thus, 3.66% decrease in
the agricultural land was evident in the last two decades (Figure.4).
Regional dynamics in an industrial region of south Gujarat, india –A geospatial perspective
Bikramjit Barooah et al.,
International Journal of Geomatics and Geosciences
Volume 5 Issue 4, 2015 597
Figure 3: Landuse and landcover map of Bharuch city and its surroundings (1990)
Figure 4: Land use and land cover map of Bharuch city and its surroundings (2011)
Table 1: Percentage wise Land use and land cover category (1990-2011)
Categories
Area in km2
(1990)
% to Total
Area
Area in km2
(2011)
% to Total
Area
% Change in
Two
Decades
Vegetation 58.72 30.53 54.89 28.54 -1.99
Agriculture 79.44 41.30 72.39 37.64 -3.66
Regional dynamics in an industrial region of south Gujarat, india –A geospatial perspective
Bikramjit Barooah et al.,
International Journal of Geomatics and Geosciences
Volume 5 Issue 4, 2015 598
Figure 5: Percentage change in land use and land cover categories
(Between 1990-2011).
During this period the percentage of land under vegetation cover (-1.04%), water bodies
(-1.15%) and coastal sediments (-1.04%) also declined. No significant change (0.05%) was
observed in the fallow land (Figure. 5). Normalized Difference Vegetation Index showed that
during 1990 the data ranged between +0.556 to -0.144 while in 2011 (Figure. 6a and 6b), it
varied between +0.049 to -0.067. Normalized Difference Built-up Index (NDBI) depicts the
changes that took place in-terms of buildup land consisting of Shortwave Infrared and
Near Infrared wavelength. In 1990, the NDBI ranged between +0.393 to -0.575 while in 2011
it varied between +0.513 to -0.538 (Figure. 6c and 6d).
b
Buildup Land 15.62 8.12 30.49 15.85 7.73
Fallow Land 11.31 5.88 11.40 5.93 0.05
Water Body 14.32 7.45 12.11 6.30 -1.15
Riverine
Sediments 12.92 6.72 10.93 5.68 -1.04
Total Area 192.33
192.33
Regional dynamics in an industrial region of south Gujarat, india –A geospatial perspective
Bikramjit Barooah et al.,
International Journal of Geomatics and Geosciences
Volume 5 Issue 4, 2015 599
a
c d
e f
g h
Figure 6: Band rationing indices (a and b-NDVI of 1990 and 2011, c and d-NDBI of 1990
and 2011, e and f- NDWI of 1990 and 2011, g and h-NDBaI of 1990 and 2011)
A significant change was noted on the positive side while in the lower side, value did not
showed any important variation. Reversely, NDWI depicted changes that took place in last 21
years considering NIR and SIR (Figure. 6e and 6f). The bareness index (NDBaI) was
computed by using SWIR and TIR band rationing. During 1990, the NDBaI ranged between
+0.039 to -0.900 while during 2011, the variation was between +0.216 to -0.878. Thus, many
fold increasing trend was noted at the higher end while at the lower end a small decrease was
Regional dynamics in an industrial region of south Gujarat, india –A geospatial perspective
Bikramjit Barooah et al.,
International Journal of Geomatics and Geosciences
Volume 5 Issue 4, 2015 600
observed (Figure. 6g and 6h). Spatial distribution of the NDVI illustrated that in the central
and western portion of the study area, a big patch of higher index was observed while in the
2011 this patch shrunk. The central patch moved further north and the patch in the western
part is almost absent in the recent years. The southern segment of river Narmada experienced
a significant decrease in the vegetation cover.
4.3 Correlation among the band rationing indices
Figure 7: Coefficient of correlation matrix of different indices.
Each of the band rationing indices depicted a particular type of physical entity in respect to
the particular wavelength. Relationship among each of the index can be useful to show the
relation over certain time span. Between NDWI and NDVI, a negative correlation was
observed for both the years. In 1990, the correlation was -0.228. Perfect negative correlation
was found between NDWI and NDBI for both years. The relationship between NDVI and
NDBI was positive for both the years but relationship weakened in 2011 (+0.228 in 1990 and
+0.028 in 2011) (Figure.7). A significant change was observed among the parameters of
NDWI and NDBaI. In 1990, the correlation between them was +0.778 while in 2011 it was
+0.793. On the other hand, the correlation between NDWI and NDBaI in 1990 was -0.778
while in 2011 it became -0.793. In case of NDVI and NDBaI it was -0.653 and -0.535 in
1990 and 2011 respectively.
5. Conclusion
From the present study the following inferences could be drawn-
1. The city of Bharuch has expanded in multi-direction with 7.73% change in the last
two decades which clearly indicated the rapid process of urbanization. The major hub
of growth was observed in the northern portion of the river Narmada with few smaller
settlements that has recently emerged due to the spread effect of the city centre.
2. Fast expansion of the city centre has put it effect on different parameters (like
vegetation cover, agricultural activities and water bodies) which are located in the
close association with each other’s.
Regional dynamics in an industrial region of south Gujarat, india –A geospatial perspective
Bikramjit Barooah et al.,
International Journal of Geomatics and Geosciences
Volume 5 Issue 4, 2015 601
3. Computer aided knowledge based map depicted the changes in spatial pattern of land
use and land cover while band rationing techniques (like NDVI, NDWI, NDBaI and
NDBI) successfully enumerated individual parameters (like vegetation, water body,
bareness and built-up area).
4. Coefficient of correlation matrix showed that all the indices are in close relationship
to each other (either positive or negative) and affect each other.
6. References
1. Chen, X.-L., Zhao, H.-M., Li, P.-X., & Yin, Z.-Y., (2006), Remote sensing image-
based analysis of the relationship between urban heat island and land use/cover
changes. Remote Sensing of Environment, 104, pp 133–146.
2. DCHB, District Census Hand Book, (2001), Bharuch District, Census of India.
3. Deng, J. S., Wang, K., Hong, Y., & Qi, J. G., (2009), Spatio-temporal dynamics and
evolution of land use change and landscape pattern in response to rapid urbanization.
Landscape and Urban Planning, 92(3), pp 187-198.
4. Kivinen, S., & Kumpula, T., (2014), Detecting land cover disturbances in the Lappi
reindeer herding district using multi-source remote sensing and GIS data.
International Journal of Applied Earth Observation and Geoinformation, 27, pp 13-19.
5. Li, X., & Yeh, A. G. O, (2004), Analyzing spatial restructuring of land use patterns in
a fast growing region using remote sensing and GIS. Landscape and urban planning,
69(4), pp 335-354.
6. Li, Z., & Fox, J. M, (2012), Mapping rubber tree growth in mainland Southeast Asia
using time-series MODIS 250 m NDVI and statistical data. Applied Geography, 32,
pp 420–432.
7. Loveland, T.R., Sohl, T.L., Stehman, S.V., Gallant, A.L., Sayler, K.L., and Napton,
D.E, (2002), A strategy for estimating the rates of recent United States land-cover
changes.
8. Photogrammetric Engineering & Remote Sensing, 68(10), pp 1091-1099
9. Maki, M., Ishiahra, M., & Tamura, M, (2004), Estimation of leaf water status to
monitor the risk of forest fires by using remotely sensed data. Remote Sensing of
Environment, 90, pp 441–450.
10. Prakash, A., & Gupta, R. P, (1998), Land-use mapping and change detection in a coal
mining area-a case study in the Jharia coalfield, India. International journal of remote
sensing, 19(3), pp 391-410.
11. Porter-Bolland, L., Ellis, E. A., & Gholz, H. L (2007). Land use dynamics and
landscape history in La Montaña, Campeche, Mexico, Landscape and Urban Planning,
82(4), pp 198-207.
Regional dynamics in an industrial region of south Gujarat, india –A geospatial perspective
Bikramjit Barooah et al.,
International Journal of Geomatics and Geosciences
Volume 5 Issue 4, 2015 602
12. Rawat, J. S., Kumar, M., & Biswas, V, (2013), An integrated approach of Remote
Sensing and GIS for land use/cover change detection: A case study of Bhimtal tourist
town (India). Bulletin of Environmental and Scientific Research, 2(2-3), pp 1-6.
13. Rogan, J., & Yool, S. R., (2001), mapping fire-induced vegetation depletion in the
Peloncillo Mountains, Arizona and New Mexico. International Journal of Remote
Sensing, 22(16), pp 3101-3121.
14. Yang, X., & Lo, C. P, (2002), using a time series of satellite imagery to detect land
use and land cover changes in the Atlanta, Georgia metropolitan area. International
Journal of Remote Sensing, 23(9), pp 1775-1798
15. Zhang, Y., Odeh, I. O. A., & Han, C, (2009), Bi-temporal characterization of land
surface temperature in relation to impervious surface area, NDVI and NDBI, using a
subpixel
16. Image analysis, International Journal of Applied Earth Observation and
Geoinformation, 11, pp 256–264.
17. Zhu, Z., & Woodcock, C. E., (2014), automated cloud, cloud shadow, and snow
detection in multitemporal Landsat data: An algorithm designed specifically for
monitoring land cover change. Remote Sensing of Environment, 152, pp 217-234.